Diagnosis and/or prognosis of disease is based on a myriad of factors, both objective and subjective, including but not limited to symptoms, laboratory test values, demographic factors and environmental factors. Diagnosis and/or prognosis relies on a clinician such as a physician or a veterinarian being able to identify and evaluate the relevant factors. Often this task can be difficult, and becomes exceedingly more so as the number of factors to be considered increases.
An example of a disease whose diagnosis or prognosis is difficult is cancer. Cancer may be diagnosed or prognosis developed on the basis of clinical presentation, routine histology, immunohistochemistry and electron microscopy. However, the histological appearance may not reveal the genetic aberrations or underlying biologic processes that contribute to the malignancy. Monitoring global gene expression levels using DNA microarrays could provide an additional tool for elucidating tumor biology as well as the potential for molecular diagnostic classification of cancers. Several studies have demonstrated that gene expression profiling using DNA microarrays is able to classify tumors with a high accuracy, and discover new cancer classes.
In clinical practice, several techniques are used for diagnosis or prognosis, including immunohistochemistry, cytogenetics, interphase fluorescence in situ hybridization and reverse transcription (RT)-PCR. Immunohistochemistry allows the detection of protein expression, but it can only examine one protein at a time. Molecular techniques such as RT-PCR are used increasingly for diagnostic confirmation following the discovery of tumor-specific translocations such as EWS-FLI1; t(11;22)(q24;q12) in EWS, and the PAX3-FKHR; t(2;13)(q35;q14) in alveolar rhabdomyosarcoma (ARMS). However, molecular markers do not always provide a definitive diagnosis or prognosis, as on occasion there is failure to detect the classical translocations, due to either technical difficulties or the presence of variant translocations.
DNA microarray technology is a recently developed high throughput technology for monitoring gene expression at the transcription level. Its use is akin to performing tens of thousands of northern blots simultaneously, and has the potential for parallel integration of the expression levels of an entire genome. A DNA microarray includes DNA probes immobilized on a solid support such as a glass microscope slide. The DNA probes can be double stranded cDNA or short (25 mers) or long (50-70 mers) oligonucleotides of known sequences. An ideal DNA microarray should be able to interrogate all of the genes expressed in an organism.
In DNA microarrays using cDNA, the probes are PCR amplified from plasmid cDNA clones that have been purified and can be robotically printed onto coated glass slides. DNA microarrays using oligonucleotides have an advantage over cDNA microarrays because physical clones are not necessary. The oligonucleotides can either be previously synthesized and printed on glass slides, or can be synthesized directly on the surface of silicon or glass slides. Several print-ready oligonucleotide (60-70 mers) sets are commercially available for human, mouse and other organisms (http://www.cgen.com, http://www.operon.com).
Another technique for fabricating oligonucleotides microarrays chemically synthesizes the oligonucleotides (25 mers) on a silicon surface using photolithography techniques. (Affymetrix Inc., Santa Clara, Calif.). Originally such arrays were designed to detect single-nucleotide mutations, but now have applications for gene expression profiling studies. Yet another technique delivers single nucleic acids, which ultimately form longer oligonucleotides (60 mers), by ink-jet onto glass surfaces.
One method of utilizing gene expression data from microarrays is given by Tusher et al., PNAS 98(9) p. 5116-21, April, 2001. The method of Tusher et al. is a statistical method titled Significance Analysis of Microarrays (“SAM”). The general approach in SAM is based on commonly used statistical tests, t-tests specifically, to find genes that discriminate between two classes in a gene-by-gene fashion. SAM uses replication of experiments to assign a significance to the discriminating genes in terms of a false discover rate. SAM therefore offers a method of choosing particular genes from a set of gene expression data, but does not offer a diagnosis based on those genes.
Gene-expression profiling using DNA microarrays may permit a simultaneous analysis of multiple markers, and can be used for example to categorize cancers into subgroups or provide other information concerning the relationship of the gene expression profile and the disease state. The only limitation associated with the use of DNA microarrays is the vast amount of data generated thereby. A method that would allow for the easy and automated use of DNA microarray data in disease diagnosis or prognosis is therefore desirable. Therefore, there remains a need for a method of using gene expression data to diagnose, predict, or prognosticate about a disease condition.